The multiscale hydrology, processes and intelligence (http://water.engr.psu.edu/shen/
) in Civil & Environmental Engineering at the Pennsylvania State University is recruiting multiple postdoc scholars for incoming projects. The research topics include physics-informed machine learning for hydrologic prediction, e.g., streamflow for Southwestern US and floodcasting using machine learning in the context of the National Water Model (NWM). The candidates need to have a strong mathematical and coding background and a solid publication record. Machine learning (ML) experience is preferred although not mandatory. Experiences with process-based geoscientific models will be valuable. The candidates also need to be comfortable with venturing into unknown territories. The scholar will work with state-of-the-art ML algorithms as well as a deep integration between physics and ML, and will likely interact with a large group of researchers. The MHPI is a frontier in the area of ML in hydrology and has been leading many novel developments. If you are interested, please browse our website, publications, and send inquiries to firstname.lastname@example.org.
ps. my internet connectivity may be limited in the next 2 weeks, so my reply may be delayed, but I will reply when I can.
Department of Civil and Environmental Engineering
231C Sackett Building
The Pennsylvania State University
University Park, PA 16802
Our group on AGU TV: https://www.youtube.com/watch?v=BIBBlM0BWaU
Promoting a deep integration between ML and processes, represented in our recent paper on differentiable parameter learning (https://rdcu.be/czqFD